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1/ Offline computer-use models are here. Today, one running on our Mac made it all the way to hotel checkout. We ran Qwen3.8-27B from Qwen through Cua Driver. It also turned a flyer into a Calendar event and reconciled receipts. Every model token stayed on the Mac 🧵

Francesco

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27,126 Aufrufe • vor 4 Tagen •via X (Twitter)

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1/ Today was the first time offline computer use felt real to us. We ran Muse Glimmer 30B from AI at Meta locally and watched it operate Notes and Reminders on macOS through Cua Driver We didn’t expect a 30B local model to get this far 🧵

1/ Today was the first time offline computer use felt real to us. We ran Muse Glimmer 30B from AI at Meta locally and watched it operate Notes and Reminders on macOS through Cua Driver We didn’t expect a 30B local model to get this far 🧵

Francesco

30,081 Aufrufe • vor 9 Tagen

what if i told you... computer use can be faster on local models moondream3 with its photon update today that gives it mac support can see your screen and use it with 1s latency, ty vik here we have whisper+qwen+moondream triple model pipeline working offline flawlessly

what if i told you... computer use can be faster on local models moondream3 with its photon update today that gives it mac support can see your screen and use it with 1s latency, ty vik here we have whisper+qwen+moondream triple model pipeline working offline flawlessly

murat 🍥

390,614 Aufrufe • vor 3 Monaten

Every big MoE model I test on my 3090 is basically to see how good it is compared to Qwen3.6-27B. "How good is it compared to Qwen3.6-27B?" is the question I get asked the most, and that shows how much people love and, more importantly, trust the 27B 27B is battle tested. It has proven its worth and has nothing left to prove. Now it just needs to make way for the next 27B. Here’s an example video: a Three.js task, same prompt, not oneshot but two shots for all models. All ran on my 3090 + 64GB VRAM Qwen-3.6-27b fully on vram while others have experts offloaded to system ram. Pinned on the left: Qwen3.6-27B-Q5 On the right, i'm switching between Inkling-Small-Q4_K_M DSV4-Flash-0731-UD-IQ1 Ling-3.0-Flash-Q4_K_M Qwen3.6-27B nailed it. The effects and visuals are just spot on.The other models got some things right, but not everything. Second place would have to go to Inkling-Small while DSV4-Flash-0731 and Ling-3.0-Flash failed to capture the full requirements. This is also why we’re all excited for Qwen Qwen3.8-27B!

Every big MoE model I test on my 3090 is basically to see how good it is compared to Qwen3.6-27B. "How good is it compared to Qwen3.6-27B?" is the question I get asked the most, and that shows how much people love and, more importantly, trust the 27B 27B is battle tested. It has proven its worth and has nothing left to prove. Now it just needs to make way for the next 27B. Here’s an example video: a Three.js task, same prompt, not oneshot but two shots for all models. All ran on my 3090 + 64GB VRAM Qwen-3.6-27b fully on vram while others have experts offloaded to system ram. Pinned on the left: Qwen3.6-27B-Q5 On the right, i'm switching between Inkling-Small-Q4_K_M DSV4-Flash-0731-UD-IQ1 Ling-3.0-Flash-Q4_K_M Qwen3.6-27B nailed it. The effects and visuals are just spot on.The other models got some things right, but not everything. Second place would have to go to Inkling-Small while DSV4-Flash-0731 and Ling-3.0-Flash failed to capture the full requirements. This is also why we’re all excited for Qwen Qwen3.8-27B!

AJ

10,669 Aufrufe • vor 11 Tagen

Personal Computer is now available to all users in a new Perplexity Mac app. Personal Computer is an advanced version of Perplexity Computer. It operates on any Mac, running tasks across your local files, native Mac apps, the web, and Perplexity’s secure servers.

Personal Computer is now available to all users in a new Perplexity Mac app. Personal Computer is an advanced version of Perplexity Computer. It operates on any Mac, running tasks across your local files, native Mac apps, the web, and Perplexity’s secure servers.

Perplexity

383,737 Aufrufe • vor 3 Monaten

Alright guys, wow. Qwen 3.8 27B is basically Qwen 4. What we have here is one of the best one-shot shark survival games I've made with any model. I ran this same prompt through fable, and the biggest thing all models struggle with in this test is the top-down view of the sharks. Fable even had all of the fins reversed and details jumbled on the first turn. Before now, it was really a 2 to 8 turn test to get something final regardless of model. Qwen 3.8 27B just nailed this in one output, no harness at all, just purely the model thoroughly thinking before outputting the final result. On every asset used, different shark variants, everything, it all looks incredibly tight. The boat movement, shark speed and engagement, everything is so much better than I expected, and I was expecting a lot. Imagine what 8 turns would do in a harness? This confirms to me that while Qwen 3.8 27B thinks a lot, for the first time in a local model on the base weights, IT MIGHT ACTUALLY BE WORTH IT This is a Frontier Lab model result, and it ran on my 5090 at 60tps in Q5_K_M. I am just in complete astoundment at what Qwen has accomplished here, and I am incredibly grateful that this model is open source. The speed will also increase drastically with optimisations. This is just an incredible day. What a blessing. More to come!

Alright guys, wow. Qwen 3.8 27B is basically Qwen 4. What we have here is one of the best one-shot shark survival games I've made with any model. I ran this same prompt through fable, and the biggest thing all models struggle with in this test is the top-down view of the sharks. Fable even had all of the fins reversed and details jumbled on the first turn. Before now, it was really a 2 to 8 turn test to get something final regardless of model. Qwen 3.8 27B just nailed this in one output, no harness at all, just purely the model thoroughly thinking before outputting the final result. On every asset used, different shark variants, everything, it all looks incredibly tight. The boat movement, shark speed and engagement, everything is so much better than I expected, and I was expecting a lot. Imagine what 8 turns would do in a harness? This confirms to me that while Qwen 3.8 27B thinks a lot, for the first time in a local model on the base weights, IT MIGHT ACTUALLY BE WORTH IT This is a Frontier Lab model result, and it ran on my 5090 at 60tps in Q5_K_M. I am just in complete astoundment at what Qwen has accomplished here, and I am incredibly grateful that this model is open source. The speed will also increase drastically with optimisations. This is just an incredible day. What a blessing. More to come!

Kyle Hessling

24,917 Aufrufe • vor 5 Tagen

THIS DEVELOPER RAN THE LARGEST AI MODEL IN THE WORLD ON 5 MAC STUDIOS - AND IT COST 100X LESS THAN WHAT OPENAI USES 27:47 he says it after hours of setup - Llama 3.1 405B running locally on five Mac Studios - a model that normally requires 42 GPUs at $1,600 each or an entire data center 320GB unified memory combined - every Mac Studio adds its 64GB into one shared pool - and a model that needs 1TB of VRAM just distributes across all five it launched, it responded and not a single token went to OpenAI or Google - everything local, everything private, everything yours and 100x cheaper 5 Mac Studios, 46 watts for all of them in idle - less than one light bulb per machine what OpenAI keeps behind a subscription he ran at home with a one-time purchase in a few clicks

THIS DEVELOPER RAN THE LARGEST AI MODEL IN THE WORLD ON 5 MAC STUDIOS - AND IT COST 100X LESS THAN WHAT OPENAI USES 27:47 he says it after hours of setup - Llama 3.1 405B running locally on five Mac Studios - a model that normally requires 42 GPUs at $1,600 each or an entire data center 320GB unified memory combined - every Mac Studio adds its 64GB into one shared pool - and a model that needs 1TB of VRAM just distributes across all five it launched, it responded and not a single token went to OpenAI or Google - everything local, everything private, everything yours and 100x cheaper 5 Mac Studios, 46 watts for all of them in idle - less than one light bulb per machine what OpenAI keeps behind a subscription he ran at home with a one-time purchase in a few clicks

Noisy

21,553 Aufrufe • vor 1 Monat

You can now run Qwen 3.6 27B on <10GB of memory and retain 90% of the intelligence. Today, PrismML dropped Bonsai 27B - which takes their unique compression approach they have already successfully applied to Qwen3.6 8B and Flux2-Klein and applied it to a properly smart model. Here is it using an entire computer for deep research. Long horizon agentic calls already are strong with Qwen3.6 27B and they still are here - just on 10x less memory.

You can now run Qwen 3.6 27B on <10GB of memory and retain 90% of the intelligence. Today, PrismML dropped Bonsai 27B - which takes their unique compression approach they have already successfully applied to Qwen3.6 8B and Flux2-Klein and applied it to a properly smart model. Here is it using an entire computer for deep research. Long horizon agentic calls already are strong with Qwen3.6 27B and they still are here - just on 10x less memory.

Tim Carambat

62,879 Aufrufe • vor 1 Monat

1/ Singapore, you turned our event into something truly unforgettable. From the moment you stepped through KOMA’s 20m Torii gates, we knew it would be remarkable. The venue was a dream, but it was your energy that made it extraordinary. Let’s relive it together 🧵👇

1/ Singapore, you turned our event into something truly unforgettable. From the moment you stepped through KOMA’s 20m Torii gates, we knew it would be remarkable. The venue was a dream, but it was your energy that made it extraordinary. Let’s relive it together 🧵👇

Azuki Singapore

42,913 Aufrufe • vor 1 Jahr

local LLMs on Mac are getting good. but the workflow around models is still messy. you have models in: •⁠ ⁠LM Studio •⁠ ⁠Ollama •⁠ ⁠Hugging Face cache •⁠ ⁠random folders •⁠ ⁠manual downloads and every runtime expects something slightly different. we built ModelHub to clean up this layer. it’s a native macOS menu bar app for discovering, downloading, and managing local LLMs. not a replacement for Ollama, oMLX or LM Studio. it sits beside them. Ollama = run models MLX = Apple Silicon acceleration LM Studio = local model workspace Hugging Face = model ecosystem ModelHub = model discovery + management layer we’re live on Product Hunt today. would love feedback from people running local models on Mac.

local LLMs on Mac are getting good. but the workflow around models is still messy. you have models in: •⁠ ⁠LM Studio •⁠ ⁠Ollama •⁠ ⁠Hugging Face cache •⁠ ⁠random folders •⁠ ⁠manual downloads and every runtime expects something slightly different. we built ModelHub to clean up this layer. it’s a native macOS menu bar app for discovering, downloading, and managing local LLMs. not a replacement for Ollama, oMLX or LM Studio. it sits beside them. Ollama = run models MLX = Apple Silicon acceleration LM Studio = local model workspace Hugging Face = model ecosystem ModelHub = model discovery + management layer we’re live on Product Hunt today. would love feedback from people running local models on Mac.

sabesh 📟

31,497 Aufrufe • vor 2 Monaten

Today we're excited to launch Action: a Claude computer use launcher for macOS. Action is a macOS launcher that can take actions (click, type, and more) on your Mac using Claude’s computer use API. The interface is a floating window triggered by a keyboard shortcut, similar to Spotlight. This lets you see what the model outputs as it performs actions.

Today we're excited to launch Action: a Claude computer use launcher for macOS. Action is a macOS launcher that can take actions (click, type, and more) on your Mac using Claude’s computer use API. The interface is a floating window triggered by a keyboard shortcut, similar to Spotlight. This lets you see what the model outputs as it performs actions.

Lawrence Chen

21,622 Aufrufe • vor 1 Jahr

while we don’t know for sure if the iPhone mirroring feature will come to the Apple Vision Pro, I sure hope it does. for now, it's available on Mac—i tested it and it's amazing! here’s how you can experience it on your Mac today: 1. make sure both your iPhone and Mac are running the latest beta versions of iOS 18 and macOS Sequoia. 2. position your iPhone near your Mac. 3. an icon titled "iPhone Mirroring" will appear on your Mac's Dock. 4. select the "Continue" button on your Mac. 5. unlock your iPhone when prompted. 6. on your Mac, click the "Get Started" button. 7. with your iPhone locked, open the "iPhone Mirroring" app on your Mac. this feature is a game-changer, and i can’t wait for the day it hopefully comes to the Vision Pro!

while we don’t know for sure if the iPhone mirroring feature will come to the Apple Vision Pro, I sure hope it does. for now, it's available on Mac—i tested it and it's amazing! here’s how you can experience it on your Mac today: 1. make sure both your iPhone and Mac are running the latest beta versions of iOS 18 and macOS Sequoia. 2. position your iPhone near your Mac. 3. an icon titled "iPhone Mirroring" will appear on your Mac's Dock. 4. select the "Continue" button on your Mac. 5. unlock your iPhone when prompted. 6. on your Mac, click the "Get Started" button. 7. with your iPhone locked, open the "iPhone Mirroring" app on your Mac. this feature is a game-changer, and i can’t wait for the day it hopefully comes to the Vision Pro!

Justin Ryan

70,266 Aufrufe • vor 2 Jahren

1/4 🧵Today, we launch #ParklandPam. We lived through Parkland. Pam Bondi built her career on it. She used our trauma to get a promotion. Then she betrayed every promise she made. We’re here to hold her accountable.

1/4 🧵Today, we launch #ParklandPam. We lived through Parkland. Pam Bondi built her career on it. She used our trauma to get a promotion. Then she betrayed every promise she made. We’re here to hold her accountable.

March For Our Lives

48,808 Aufrufe • vor 1 Jahr

I tested Gemma 4 12b on my Macbook Pro M5 Max with 128GB of unified memory. It runs VERY slow for a 12b parameter model. 44 tokens/second. The lava lamp that it produced was also terrible. I would stick to Qwen 3.6 27b or Qwen 3.6 35b. Google is still way behind, even with their local models.

I tested Gemma 4 12b on my Macbook Pro M5 Max with 128GB of unified memory. It runs VERY slow for a 12b parameter model. 44 tokens/second. The lava lamp that it produced was also terrible. I would stick to Qwen 3.6 27b or Qwen 3.6 35b. Google is still way behind, even with their local models.

BridgeMind

70,626 Aufrufe • vor 2 Monaten

Here's a teaser of our Mac-1 model. > 6.6B model > runs locally (on any Mac) > requires 7GB RAM (12GB ideal) > can use 487 MacOS native tools > perform multi-tool chained tasks > reasoning: ON > output: ~65 tok/s We built a robust application layer around the model to make UI/UX MacOS native. The "model-focused" SaaS era is here. Stay tuned for more.

Here's a teaser of our Mac-1 model. > 6.6B model > runs locally (on any Mac) > requires 7GB RAM (12GB ideal) > can use 487 MacOS native tools > perform multi-tool chained tasks > reasoning: ON > output: ~65 tok/s We built a robust application layer around the model to make UI/UX MacOS native. The "model-focused" SaaS era is here. Stay tuned for more.

CJ Zafir

1,072,701 Aufrufe • vor 2 Monaten

APPLE BUILT A $10,000 COMPUTER THAT RUNS WHAT USED TO REQUIRE AN ENTIRE SERVER ROOM 06:40 he shows the number that breaks everything - one Mac Studio is more powerful than four Dell or AMD machines combined and uses half the energy DeepSeek R1 doesn't run on even two competitors - you need four - one Mac Studio does it alone runs quieter than a fan in the room and uses less energy than a kettle in the kitchen four Mac Studios on a desk connected through a regular cable run a 1 trillion parameter model - what used to require an entire data center anyone can put this at home and Jeff Geerling proved it - run the same models that big companies use without cloud, without subscriptions, without data leaving the room

APPLE BUILT A $10,000 COMPUTER THAT RUNS WHAT USED TO REQUIRE AN ENTIRE SERVER ROOM 06:40 he shows the number that breaks everything - one Mac Studio is more powerful than four Dell or AMD machines combined and uses half the energy DeepSeek R1 doesn't run on even two competitors - you need four - one Mac Studio does it alone runs quieter than a fan in the room and uses less energy than a kettle in the kitchen four Mac Studios on a desk connected through a regular cable run a 1 trillion parameter model - what used to require an entire data center anyone can put this at home and Jeff Geerling proved it - run the same models that big companies use without cloud, without subscriptions, without data leaving the room

Noisy

26,153 Aufrufe • vor 1 Monat

We’re launching AI Spotlight, a new series diving into Qwen’s models and AI products. First up: Qwen3.8-Max and what makes it different. This is just the first; more videos are coming. Stay tuned! Until then, check it out on: Qwen Cloud: Model Studio: #AISpotlight #Qwen3_8Max #AlibabaCloud #Multimodal #AgenticAI

We’re launching AI Spotlight, a new series diving into Qwen’s models and AI products. First up: Qwen3.8-Max and what makes it different. This is just the first; more videos are coming. Stay tuned! Until then, check it out on: Qwen Cloud: Model Studio: #AISpotlight #Qwen3_8Max #AlibabaCloud #Multimodal #AgenticAI

Alibaba Cloud

34,503 Aufrufe • vor 5 Tagen

The most downloaded AI on earth is now Chinese. Alibaba just gave away a model that matches Claude's flagship, and it literally runs on a $700 used graphics card. The Qwen models crossed 3 BILLION downloads in six months. Hugging Face counted 418 million downloads for Google this year, and 227 million for Meta. Alibaba cleared more than four times both of them combined. Then today it released Qwen3.8-27B under an Apache 2.0 license. The model has 27 billion parameters, native vision, and a 262,000 token context window. Developers are running it locally on 17 gigabytes of memory, on used cards that cost a few hundred dollars. Alibaba's own benchmark table claims it beats Opus 4.6 Max on computer use by 84.3 to 72.7, on mobile use by 81.9 to 62, and on visual math by 94.6 to 65.5. Those numbers come from the vendor and nobody has independently verified them yet, so treat them as a claim. But the generation over generation jumps are harder to wave away: On DeepSWE the score went from 13.3 to 42.2. On software engineering it went from 49.3 to 79.0. That happened in ONE release cycle. And Apache 2.0 means anyone can download the weights, modify them, build products on them, sell those products, and never pay or ask permission. It cannot be revoked. Once the file is on your drive it is yours permanently. 3 billion downloads means those files already sit on machines in every country on Earth. Alibaba could delete everything tomorrow and it would change nothing. Washington spent 4 years building an export control regime around chips, model weights, and entity lists. Every piece of it assumes a chokepoint exists somewhere. A fab, a shipment, a company that can be told no. But there is no chokepoint for a file that has already been copied three billion times. And the copying compounds. Hugging Face counted 151,448 models built on top of Qwen, which is 2.6x Meta's entire footprint and 4.7x the number of Llama repositories. New ones appear at roughly 200 a day. The report says Qwen has become "part of the default workflow for developers deciding what models to fine-tune and deploy." Alibaba is also pushing Qwen through its cloud into Southeast Asia and Africa, markets where American labs have almost no presence, and where a very large share of the next generation of developers will learn to build. Meta and Nvidia have both rushed out new open models in recent weeks. That is what a response looks like when you feel the floor move. And to be clear, these are download and derivative numbers, not usage numbers. ChatGPT and Claude cannot be downloaded at all, so they do not appear in this comparison. What the figures measure is what developers choose to build on top of, which is a different question from what consumers type into a box. That is also why it matters MORE. Consumer habits change in an afternoon. Infrastructure choices last a decade, because everything built on top has to be rewritten to undo them. The American labs are valued on an assumption that frontier intelligence stays scarce, expensive, and rented by the token. Alibaba just made a version of it free, permanent, and small enough to run on hardware people already own. You will not get an announcement when the software you use every day starts running on a Chinese model underneath. Go and count how many of the tools you rely on could be rebuilt on free weights this year.

The most downloaded AI on earth is now Chinese. Alibaba just gave away a model that matches Claude's flagship, and it literally runs on a $700 used graphics card. The Qwen models crossed 3 BILLION downloads in six months. Hugging Face counted 418 million downloads for Google this year, and 227 million for Meta. Alibaba cleared more than four times both of them combined. Then today it released Qwen3.8-27B under an Apache 2.0 license. The model has 27 billion parameters, native vision, and a 262,000 token context window. Developers are running it locally on 17 gigabytes of memory, on used cards that cost a few hundred dollars. Alibaba's own benchmark table claims it beats Opus 4.6 Max on computer use by 84.3 to 72.7, on mobile use by 81.9 to 62, and on visual math by 94.6 to 65.5. Those numbers come from the vendor and nobody has independently verified them yet, so treat them as a claim. But the generation over generation jumps are harder to wave away: On DeepSWE the score went from 13.3 to 42.2. On software engineering it went from 49.3 to 79.0. That happened in ONE release cycle. And Apache 2.0 means anyone can download the weights, modify them, build products on them, sell those products, and never pay or ask permission. It cannot be revoked. Once the file is on your drive it is yours permanently. 3 billion downloads means those files already sit on machines in every country on Earth. Alibaba could delete everything tomorrow and it would change nothing. Washington spent 4 years building an export control regime around chips, model weights, and entity lists. Every piece of it assumes a chokepoint exists somewhere. A fab, a shipment, a company that can be told no. But there is no chokepoint for a file that has already been copied three billion times. And the copying compounds. Hugging Face counted 151,448 models built on top of Qwen, which is 2.6x Meta's entire footprint and 4.7x the number of Llama repositories. New ones appear at roughly 200 a day. The report says Qwen has become "part of the default workflow for developers deciding what models to fine-tune and deploy." Alibaba is also pushing Qwen through its cloud into Southeast Asia and Africa, markets where American labs have almost no presence, and where a very large share of the next generation of developers will learn to build. Meta and Nvidia have both rushed out new open models in recent weeks. That is what a response looks like when you feel the floor move. And to be clear, these are download and derivative numbers, not usage numbers. ChatGPT and Claude cannot be downloaded at all, so they do not appear in this comparison. What the figures measure is what developers choose to build on top of, which is a different question from what consumers type into a box. That is also why it matters MORE. Consumer habits change in an afternoon. Infrastructure choices last a decade, because everything built on top has to be rewritten to undo them. The American labs are valued on an assumption that frontier intelligence stays scarce, expensive, and rented by the token. Alibaba just made a version of it free, permanent, and small enough to run on hardware people already own. You will not get an announcement when the software you use every day starts running on a Chinese model underneath. Go and count how many of the tools you rely on could be rebuilt on free weights this year.

Ricardo

78,439 Aufrufe • vor 2 Tagen

We have been heads down on something for the past 5 months (which is a very long time in startup time). Today we are finally sharing it. Vy, our AI agent that uses your computer for you, is now cross platform. It runs on both Mac and Windows, sees your screen, moves the mouse, types, scrolls, and clicks through apps. No APIs or custom integrations needed.

We have been heads down on something for the past 5 months (which is a very long time in startup time). Today we are finally sharing it. Vy, our AI agent that uses your computer for you, is now cross platform. It runs on both Mac and Windows, sees your screen, moves the mouse, types, scrolls, and clicks through apps. No APIs or custom integrations needed.

Vercept

753,560 Aufrufe • vor 8 Monaten

Mac Mini is super nice because you can use native iMessage for talking with OpenClaw. The only problem is that it's very expensive and you need to always have your Mac Mini on to run this setup. So I made a plugin to allow anyone on any computer (either Linux, Windows, or Mac) to be able to have access to iMessage natively. Even better, it has access to all of the reactions, triple dot indicator, etc. Check it out and let me know what you think.

Mac Mini is super nice because you can use native iMessage for talking with OpenClaw. The only problem is that it's very expensive and you need to always have your Mac Mini on to run this setup. So I made a plugin to allow anyone on any computer (either Linux, Windows, or Mac) to be able to have access to iMessage natively. Even better, it has access to all of the reactions, triple dot indicator, etc. Check it out and let me know what you think.

Nick Vasilescu

265,771 Aufrufe • vor 6 Monaten

APPLE RESEARCH SCIENTIST JUST SHOWED HOW 4 MAC STUDIOS RUN A TRILLION PARAMETER MODEL LOCALLY ZERO COSTS 13:18 she shows the main thing - connect 4 Mac Studios and you get 1TB of shared memory - exactly enough to run a trillion parameter model right on your desk Apple's library - and four machines start working as one cluster tensor parallelism: every machine holds part of every layer - all process the same token simultaneously - speed increases 3x compared to a single device fine-tuning: one Mac Studio processes 180 tokens per second four together process 600 and not a single byte of data leaves the room one command in the terminal - and a trillion parameter model answers from your desk and runs 24/7 data centers took years to build to run models like this - Apple did it with four Thunderbolt cables

APPLE RESEARCH SCIENTIST JUST SHOWED HOW 4 MAC STUDIOS RUN A TRILLION PARAMETER MODEL LOCALLY ZERO COSTS 13:18 she shows the main thing - connect 4 Mac Studios and you get 1TB of shared memory - exactly enough to run a trillion parameter model right on your desk Apple's library - and four machines start working as one cluster tensor parallelism: every machine holds part of every layer - all process the same token simultaneously - speed increases 3x compared to a single device fine-tuning: one Mac Studio processes 180 tokens per second four together process 600 and not a single byte of data leaves the room one command in the terminal - and a trillion parameter model answers from your desk and runs 24/7 data centers took years to build to run models like this - Apple did it with four Thunderbolt cables

Noisy

48,615 Aufrufe • vor 1 Monat